This paper investigates the forecasting power of stock prices using two methods, namely, the random walk and the non-parametric methods. Using daily prices of the FTSE/JSE All Share index it is found that non-parametric methodology reveals distributional behaviour in the time series that is not...

This paper proposes an asymptotic one-sided N(0, 1) test for independence between two stationary time series using the empirical characteristic function. Unlike the tests based on the cross-correlation function (e.g. Haugh, 1976; Hong, 1996; Koch & Yang 1986), the proposed test has power against all...

Solar radiation is the principal and fundamental energy for many physical, chemical and biological processes. However, it is measured at a very limited number of meteorological stations in the world. This paper presented the methods of monthly mean daily solar radiation estimation using support...

With the fast development of financial products and services, bank’s credit departments collected large amounts of data, which risk analysts use to build appropriate credit scoring models to evaluate an applicant’s credit risk accurately. One of these models is the Multi-Criteria...

In this paper, the local polynomial fit based on the kernel weighted local-likelihood function and the location of the change point is considered as an estimator for the regression function or its νth derivative. Using the data sets split by the location, we estimate the left and right parts of...

This study employs a parametric approach based on TGARCH and GARCH models to estimate the VaR of the copper futures market and spot market in China. Considering the short selling mechanism in the futures market, the paper introduces two new notions: upside VaR and extreme upside risk spillover....

The mean shift (MS) algorithm is a non-parametric, iterative technique that has been used to find modes of an estimated probability density function (pdf). Although the MS algorithm has been widely used in many applications, such as clustering, image segmentation, and object tracking, a rigorous...

We propose new over-identifying restriction (OIR) tests that are robust to heteroskedasticity and serial correlations of unknown form. The proposed tests do not require consistent estimation of the asymptotic covariance matrix and hence avoid choosing the bandwidth in nonparametric kernel...

Under the condition that the observations, which come from a high-dimensional population (X,Y), are strongly stationary and strongly-mixing, through using the local linear method, we investigate, in this paper, the strong Bahadur representation of the nonparametric M-estimator for the unknown...